Next-Gen Drug Discovery with AI
DeepBio Academy delivers 10 comprehensive modules spanning Cheminformatics, Structural Bioinformatics, CADD docking, System Biology, GROMACS 100ns molecular dynamics, PyTorch GNNs, and In Silico Toxicology on Google Colab GPUs.
Curriculum Framework
Target-to-Lead Roadmap
Why NextGen Drug Discovery with AI?
DeepBio Academy bridges the gap between computational chemistry, structural bioinformatics, and artificial intelligence. We train students and researchers with the exact skills, pipelines, and reproducible codebases required in pharmaceutical R&D and top international labs.
Cloud-First & Zero Local Setup
Every session and assignment runs in Google Colab with free GPU acceleration. No expensive $5,000 workstations or broken Linux dependencies — start coding in minutes.
Cheminformatics & CADD Docking
Master the industry standard: RDKit molecular graphs, ChEMBL/PubChem APIs, PDB active site preparation, and AutoDock Vina high-throughput virtual screening.
100ns GROMACS MD & GNNs
Simulate dynamic protein-ligand stability over 100ns trajectories, compute RMSD/RMSF curves, and train Graph Neural Networks in PyTorch Geometric.
Lab Recruitment & Q1 Papers
Top performers from the capstone project are invited to join DeepBio's research lab as Research Assistants to co-author peer-reviewed publications.
NextGen Drug Discovery Curriculum Roadmap
10 progressive modules engineered to take you from foundational scientific computing and cheminformatics to molecular dynamics, graph neural networks, in silico toxicology, and capstone translation.
Foundations of Drug Discovery & Scientific Python
Master scientific Python (NumPy, Pandas, Matplotlib), Google Colab free GPU workflows, and core biochemical thermodynamics.
Cheminformatics & Molecular Data Science
Represent and manipulate small molecules with RDKit, compute Morgan fingerprints, parse SMILES, and mine ChEMBL databases.
Structural Bioinformatics
Retrieve 3D crystal structures from PDB, prepare active sites, detect cryptic binding pockets, and visualize complexes with py3Dmol.
Computer-Aided Drug Design (CADD)
High-throughput virtual screening with AutoDock Vina, grid box optimization, docking pose scoring, and PLIP interaction profiling.
System Biology
Construct protein-protein interaction (PPI) networks with STRING and Cytoscape, identify hub target genes, and run KEGG pathway enrichment.
Molecular Dynamics & Molecular Simulation
Set up and run 100ns GROMACS molecular dynamics simulations in TIP3P water boxes, and compute RMSD/RMSF trajectory curves.
AI in Drug Discovery
Train machine learning models for bioactivity (pIC50) prediction, perform scaffold splitting, and define applicability domains with SHAP.
Deep Learning for Molecular Modeling
Model molecules as topological graphs using PyTorch Geometric (GCN/GAT) and predict structures with AlphaFold2 & ColabFold.
AI in In Silico Toxicology Modeling
Predict ADMET pharmacokinetic profiles, blood-brain barrier permeability, and hERG channel cardiotoxicity to de-risk leads early.
Integrated Drug Discovery Workflow
Execute an end-to-end target-to-lead pipeline from disease target to validated lead, producing a publication-ready research report.
Live Google Colab Notebooks in Every Session
No broken local dependencies or expensive hardware needed. Every student receives research-grade, fully commented Google Colab notebooks ready to run with free GPU acceleration.
Cheminformatics & ChEMBL Bioactivity Mining
Mining EGFR inhibitors from ChEMBL, computing Lipinski Rule of 5 descriptors, generating Morgan fingerprints (ECFP4), and filtering chemical libraries in Python.
# 1. Install & import RDKit in Google Colab
!pip install -q rdkit-pypi chembl_webresource_client
from rdkit import Chem
from rdkit.Chem import Descriptors, AllChem
from chembl_webresource_client.new_client import new_client
import pandas as pd
# Fetch bioactive compounds against target EGFR (CHEMBL203)
target = new_client.target.filter(target_chembl_id='CHEMBL203')
activity = new_client.activity.filter(target_chembl_id='CHEMBL203', standard_type='IC50')
df = pd.DataFrame.from_dict(activity)
# Compute Lipinski's Rule of 5 Descriptors
def calculate_lipinski(smiles):
mol = Chem.MolFromSmiles(smiles)
return {
'MW': Descriptors.MolWt(mol),
'LogP': Descriptors.MolLogP(mol),
'HBD': Descriptors.NumHDonors(mol),
'HBA': Descriptors.NumHAcceptors(mol)
}
print(f"✓ Retrieved {len(df)} verified bioactivity data points from ChEMBL")Free Cloud GPUs
Execute GROMACS simulations and PyTorch training on free NVIDIA Colab GPUs with zero local workstation spend.
100% Reproducible
Every notebook is fully documented, tested, and ready for publication-grade research outputs.
36 Complete Notebooks
Get an individual, structured Colab workbook for every single live lecture and weekly assignment.
Lifetime Repository Access
Maintain permanent access to all Google Colab templates, code libraries, and future cohort updates.
Projects Built on Real Research Data
Gain production-level mastery with hands-on projects designed to be showcased directly on GitHub and in your academic and industry research applications.
Oncology Kinase Inhibitor Screening
Virtual screen 50,000+ compounds against oncogenic EGFR/KRAS mutants with AutoDock Vina and validate hits.
Viral Protease Target-to-Lead Pipeline
Dock and score covalent and non-covalent inhibitors against SARS-CoV-2 Mpro and Dengue NS2B-NS3 protease.
100ns Protein-Ligand MD Simulation
Solvate complexes in TIP3P water boxes, run 100ns GROMACS MD, and calculate RMSD/RMSF stability curves.
Graph Neural Network Bioactivity Predictor
Build a PyTorch Geometric GNN (GCN/GAT) that predicts IC50 bioactivity directly from 2D molecular graphs.
ADMET & Blood-Brain Barrier (BBB) ML Model
Train classification models to forecast blood-brain barrier permeability and hERG cardiotoxicity endpoints.
AlphaFold Structure Modeling & Pocket Mapping
Predict uncharacterized target structures with ColabFold and map allosteric druggable binding pockets.
Industry & Academic Standard Software
Every session uses the exact toolkits and cloud environments utilized across biopharma and top computational laboratories.
Who Should Join This Program?
Built for ambitious students, graduate researchers, and industry scientists stepping into in silico therapeutics.
Program Structure & Schedule
Transparent month-to-month fee. Zero hardware setup costs.
What You Will Walk Away Able to Do
By the end of the program you will have practical, portfolio-ready command of the full computational drug discovery pipeline.
Featured Leadership & Mentors
Learn directly from experienced bioinformaticians, computational chemists, and AI researchers guiding every live session.
Jubayer Hossain
Lead Instructor & Mentor
DeepBio Ltd
Multiomics Scientist
Musab Shahriar
Instructor
DeepBio Academy
Computational Drug Discovery & Virtual Screening
Pritom Kundu
Instructor
DeepBio Academy
AI-driven Drug Discovery & Machine Learning
Lamia Hasan Barsha
Instructor
DeepBio Academy
Computer-Aided Drug Design & Molecular Modeling
Naem Islam Abhi
Instructor
DeepBio Academy
scRNA-seq Disease Drug Discovery & Target ID
Direct Mentorship · Research Rigor
Every student works directly with the lead instructor on live coding, weekly assignments, and publication-grade capstone projects.
5+ Years
Research in CADD & Cheminformatics
3,000+
Students & Researchers Trained
20+
Peer-Reviewed Scientific Publications
RA Pathway
Direct Lab Recruitment for High Performers
Earn an Official Verified Certificate
Complete the live sessions and capstone project to receive an official DeepBio Academy certificate of completion with verifiable digital credentials.
DeepBio Academy
Certificate of Completion
This certifies that
Your Name Here
has successfully completed the NextGen Drug Discovery with AI program, covering cheminformatics, structural bioinformatics, AutoDock Vina, GROMACS molecular dynamics, and Graph Neural Networks.
Issued
Upon Completion
Signed
Lead Instructor
Everything You Need to Know
Clear answers regarding pre-registration, software requirements, schedule, and certification.
Does pre-registration cost anything?
Do I need a programming or biology background to join?
What equipment do I need?
Are the classes live or pre-recorded?
Will I get an official verified certificate?
What is the capstone project?
How is the fee structured?
Is this program suitable for working professionals and university students?
Become the Next Generation Drug Discovery Scientist
Whether you are an undergraduate student in pharmacy/biotech, a graduate researcher, or an AI engineer, our 3-month live mentorship equips you with reproducible, submittable computational research pipelines.